surviving six sigma – what statisticians should do to not ... · surviving six sigma – what...

Surviving Six Sigma –What Statisticians Should Do

To Not Be Left BehindProfessor Tony Bendell

Director, Centre of Quality Excellence,

University of Leicester, UK

and

Managing Director, Services Ltd., Nottingham, UK

Swiss Statistics Meeting‘Quality and Statistics’

Aarau, Switzerland, 17-19 November 2004

Services Ltd., Quality & Reliability House, 82 Trent Boulevard, West Bridgford, Nottingham, NG2 5BL, UKTel: +44 115 945 5285 Fax: +44 115 981 7137 E-mail: [email protected] www.servicesltd.co.uk

www.servicesltd.co.uk

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Centre of Quality Excellence, University of Leicester

Established January 2002Mission: To foster a rigorous, yet creative approach to the development of Quality Excellence in management, engineering, systems and services across the UK and world Industry, commerce and the public and voluntary sectors.In-company, Distance Learning and campus based MSc and postgraduateprogrammes in

- The Management of Quality Excellence- Customer Service Management

Broad research base – funding from EFQM, BQF, Rolls-Royce, DTI, Patent Office, LRQA, SGS Yarsley, EMDA etc.Recognition Centre for Six Sigma Black and Green BeltsSponsor and Associate organisations


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Services Ltd.

- Excellence, TQM- ISO 9000, ISO14000 etc.- Six Sigma, Design for Six Sigma- Reliability Analysis

- Benchmarking- Creativity, HRM etc.- SPC, Taguchi, QFD, tools- Statistical Modelling, OR

Quality and Productivity Consultancy and Training organisation established 1983One of the three largest BQF licensed trainers for the EFQM Excellence Model and partner for Six SigmaIRCA licensed trainers for ISO 9001:2000Licensed by the British Accreditation Bureau etc.Specialisms:


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Six Sigma - The Threat and the Opportunity for StatisticiansSix Sigma having major business impactTrains and supports its own peopleStatistically-based, but not just statisticsShould statisticians welcome or oppose Six Sigma? How should they get involved?


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Six Sigma Success in the Late Twentieth Century

Extensively applied, growing application, high esteemManagement and Engineering press, conferences and growing academic literatureIndividual Black/Green belt first project annualised savings £10,000 - £1.5 million“The most important initiative GE has ever undertaken … it has taught GE employees about the importance of fulfilling expectations” Jack Welch

The Rewards for GESix Sigma Progress(In millions)

CostBenefit

$2500200015001000500

01996 1997 1998 1999


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Origins‘1989’ Motorola1995 GEBut a ‘clever package’, nothing much new!

origins in Deming, Juran and Ishikawa’s work from 1950’s and Shewhart from 1920’s

Deming Juran

Ishikawa Shewhart


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The Meaning of Six SigmaTechnical Term (Statistical)

12 Standard Deviations inside Specification“Best in Class”“three non-conformances out of one million opportunities”

Variation Reduction/Improvement approach that allows comparability between dissimilar processesMeasurement-based Corporate Strategy for improvement“Philosophy”


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The Technical Meaning for Six Sigma

DEFECTS DEFECTSSPECIFICATION

3 Sigma 3 Sigma

A 3 sigma qualityproduct/processhas 6 standard

deviations insidethe specification

6 Sigma 6 Sigma

A 6 sigma qualityproduct/processhas 12 standarddeviations insidethe specification

DefectsDefects Defects Defects

Yield (% Inside Spec) DPMO (defectives

per million opportunities

At ± 2 sigma 95.44% 45,600At ± 3 sigma 99.73% 2,700 At ± 4 sigma 99.9937% 63At ± 5 sigma 99.999943% 0.57At ± 6 sigma 99.999998% 0.002

Given the specification, Six Sigma Quality for a product/process metric means that more of the distribution is contained within the Specification than at the Three Sigma level.


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‘Standard’ Six Sigma Methodology Allows For a 1.5 Sigma Shift!

If the product/process mean now shifts 1.5 sigma (worse case), then:

6 Sigma 6 Sigma

3Sigma

3Sigma

DEFECTS DEFECTSSPECIFICATION

6 SIGMA

Yield (% Inside Spec) DPMO (defectives

per million

opportunitiesAt ± 2 sigma 69.15% 308,500At ± 3 sigma 93.32% 66,810 At ± 4 sigma 99.379% 6,210At ± 5 sigma 99.97673% 233At ± 6 sigma 99.99966% 3.4

1.5 sigmashift in mean


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Six Sigma InfrastructureStrategic programmeProject-by-Project improvement in ‘fenced-off’ areasClear responsibilities and authority – Champions, Owners, Black and Green BeltsBefore and after performance measurement (typically cost)140+ statistical tools and conceptsDMAIC(T) methodology: Define, Measure, Analyse, Improve, Control, (Transfer)


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Six Sigma Improvement Path66810

233

33.4 ppm.4

0.1

1

10

100

1000

10000

100000

3 4 5 6

Sigma Quality Level

Def

ect R

ate

(ppm

)

66810 ppm

6210 ppm

233 ppm

ppm

10xImprovement 30x

Improvement

70x Improvement

‘DESIGN FOR SIX SIGMA (DFSS)’

The ‘5 Sigma Wall’

‘PROJECT-BY-PROJECT’SIX SIGMA (DMAIC)


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Six Sigma Infrastructure Continued - Black and Green BeltsFull-time or Part-time (20%+) improvement engineers, managers, supervisors20/10+ days trainingExcel and Minitab (or alternative)Projects selected prior to trainingOn-site mentoringCertification


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Important ToolsGE/Six Sigma Academy, 140+ statistical tools – to define, measure, analyse, improve and controlWhat are the key ones?

- Simple data description and summarisation tools- Regression and Anova- SPC and Measurement System Analysis- Experimental Design (including Taguchi methodology, Response Surfaces)- Quick technical problem solving

Non-statistical tools- FMEA/FMECA- QFD- Poka Yoke- Right brain and creativity tools


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Not Just About Statistical ToolsProject-by-Project approach, clear metric and DMAICT crucialPrevious tool - use poor, disorganised, non-coherentMeasurement System AnalysisEmphasis on improvement, not Quality Assurance (e.g. attribute charting)Much so-called SPC is really SQC‘Control the X’s not the Y’s

- process variables not the product characteristicsExperimental Design key to successful projects


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‘Versions’ of Six SigmaManufacturing (and Engineering)Transactional

- Less DOE- Lognormal distributions

Design for Six Sigma- reduced process control emphasis- development cycle

‘Lean Sigma’- dangerous: potential conflict in methodologies


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Connections to Other Quality ApproachesISO 9001:2000EFQM Excellence ModelDesign for Six Sigma


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ISO 9001, 19944.20 Statistical TechniquesIdentification of NeedThe supplier shall identify the need for statistical techniques required for establishing, controlling and verifying process capability and product characteristicsProceduresThe supplier shall establish and maintain documented procedures to implement and control the application of the statistical techniques identified above


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Quality Management Process Model


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EFQM Excellence ModelRESULTS 50%ENABLERS 50%

KeyPerformance

Results15%

People9%

Policy &Strategy

8%

Leadership10%

Partnerships&

Resources9%

PeopleResults

9%

CustomerResults

20%Processes

14%

SocietyResults

6%

INNOVATION & LEARNING

KeyPerformance

Results

People

Policy &Strategy

Leadership

Partnerships& Resources

PeopleResults

CustomerResultsProcesses

SocietyResults

Managing VariationReduction6 Sigma

Tools: SPC D.O.E Taguchi Cpk


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Design for Six Sigma -Six Steps to Six Sigma:

1. Identify requirements of end-product2. Determine the characteristics of the components of the product that

are key to meeting the end-product requirements3. Determine for each key characteristic, the process step that affects

or controls it4. Identify target value for each characteristic that minimises impact of

variation upon end-product, and determine maximum allowable range or tolerance of that characteristic

5. Identify actual or expected variation in each characteristic anddetermine capability of relevant process step for that characteristic

6. Ensure that process steps are in statistical control and centredaround the targets


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Six Steps - Example: A TableStep One Table is balanced on its legsStep Two Legs are correct sizeStep Three Sawing processStep Four 27 inches

26.9 - 27.1 inches (allowable range)Step Five 26.8 - 27.2 inches (actual variation)

Step Six Improve sawing accuracy

CP = Allowable Range = 0.5“Actual Variation”

Designing a TableRequirements

----

Balanced on its legs

------

Component Characteristics

--

Legs arecorrect size

-------

Controlling Process Step

--

Sawing Process

-------

Target Actual

27 ± 0.1" - ± 0.2"


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Some Other IssuesDanger of dilutionConcentration on Statistical techniques and left brain‘Old-fashioned’ statisticsLack of formal link to Policy DeploymentEmphasis on Cost Down, not Profit UpCulture/Buy-in/”hype”Integration with other Programmes


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My ExperiencesVery effective focus programmeEven if infrastructure not right still effective projectsMay improve processes but not other infrastructureWhole programme should be planned and scopedVery good people development


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Toolkit and Training IssuesLearning by roteTraining requirement – Black Belts without DOE!/Danger of dilution“Left-brain only” - 141 statistical tools/ old-fashioned statistics“Right-brain” creativity/innovation tools

- e.g Six Hats, Triz etc.


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Six Sigma and the StatisticianImpact of Japanese approaches in Six Sigma on traditional engineering and statistical roles, structures and work practices

Black Belts MAY be engineers or statisticiansNeed for mind-set change

- Fact-based Data-driven decision combined with Engineering knowledge and judgement

- Cost, Customer Focus and Business Decision Making focus- Balanced treatment of variation and risk

- enfranchisement of workforce in statistical methods- questions role of ‘statistical elite’- potential move from specialists to mentors, trainers and Project

Managers- extend roles but also include others


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Are The Statisticians Included?Thin on the ground!Nervousness on “purity”Packaged solutionsIssues on role/erosion of practiceConflicts of method


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“Enfranchisement” of Statistical MethodsDo we want to see ‘correct’ use by a few or largely correct use by the many?Do we want to be ‘in’ or ‘out’?

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